<![CDATA[Preventive maintenance is an essential strategy to minimize losses due to industrial equipment failures. This study aims to develop an equipment failure prediction model using the Random Forest algorithm with the SMOTE technique to address class imbalance. The dataset used is the AI4I 2020 Predictive Maintenance Dataset with 10,000 entries and six main input variables. Preprocessing includes normalization of numerical features, one-hot encoding for categorical features, and handling of missing values. The Random Forest model was optimized using GridSearchCV and compared with K-Nearest Neighbors. Results show that Random Forest with SMOTE achieved 97% accuracy, 0.47 precision, 0.75 recall, and 0.58 F1-score on the failure class. This model outperforms KNN in detecting failures, particularly in imbalanced data. These findings contribute to the development of an early warning system to support preventive maintenance in industrial environments.]]>
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